Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues.

Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

Probing Molecular Diversity and Ultrastructure of Brain Cells with Fluorescent Aptamers.

Detergent-free immunolabeling has been proven feasible for correlated light and electron microscopy, but its application is restricted by the availability of suitable affinity reagents. Here we introduce CAptVE, a method using slow off-rate modified aptamers for cell fluorescence labeling on ultrastructurally reconstructable electron micrographs. CAptVE provides labeling for a wide range of biomarkers, offering a pathway to integrate molecular analysis into recent approaches to delineate neural circuits via connectomics.

A Scalable Staining Strategy for Whole-Brain Connectomics.

Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.

Preserving extracellular space for high-quality optical and ultrastructural studies of whole mammalian brains.

Analysis of brain structure, connectivity, and molecular diversity relies on effective tissue fixation. Conventional tissue fixation causes extracellular space (ECS) loss, complicating the segmentation of cellular objects from electron microscopy datasets. Previous techniques for preserving ECS in mammalian brains utilizing high-pressure perfusion can give inconsistent results owing to variations in the hydrostatic pressure within the vasculature. A more reliable fixation protocol that uniformly preserves the ECS throughout whole brains would greatly benefit a wide range of neuroscience studies. Here, we report a straightforward transcardial perfusion strategy that preserves ECS throughout the whole rodent brain. No special setup is needed besides sequential solution changes, and the protocol offers excellent reproducibility. In addition to better capturing tissue ultrastructure, preservation of ECS has many downstream advantages such as accelerating heavy-metal staining for electron microscopy, improving detergent-free immunohistochemistry for correlated light and electron microscopy, and facilitating lipid removal for tissue clearing.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network.

Here, we present the first analysis of the connectome of a small volume of the Octopus vulgaris vertical lobe (VL), a brain structure mediating the acquisition of long-term memory in this behaviorally advanced mollusk. Serial section electron microscopy revealed new types of interneurons, cellular components of extensive modulatory systems, and multiple synaptic motifs. The sensory input to the VL is conveyed via~1.8 × 106 axons that sparsely innervate two parallel and interconnected feedforward networks formed by the two types of amacrine interneurons (AM), simple AMs (SAMs) and complex AMs (CAMs). SAMs make up 89.3% of the~25 × 106VL cells, each receiving a synaptic input from only a single input neuron on its non-bifurcating primary neurite, suggesting that each input neuron is represented in only~12 ± 3.4SAMs. This synaptic site is likely a 'memory site' as it is endowed with LTP. The CAMs, a newly described AM type, comprise 1.6% of the VL cells. Their bifurcating neurites integrate multiple inputs from the input axons and SAMs. While the SAM network appears to feedforward sparse 'memorizable' sensory representations to the VL output layer, the CAMs appear to monitor global activity and feedforward a balancing inhibition for 'sharpening' the stimulus-specific VL output. While sharing morphological and wiring features with circuits supporting associative learning in other animals, the VL has evolved a unique circuit that enables associative learning based on feedforward information flow.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Nanobody immunostaining for correlated light and electron microscopy with preservation of ultrastructure.

Morphological and molecular characteristics determine the function of biological tissues. Attempts to combine immunofluorescence and electron microscopy invariably compromise the quality of the ultrastructure of tissue sections. We developed NATIVE, a correlated light and electron microscopy approach that preserves ultrastructure while showing the locations of multiple molecular moieties, even deep within tissues. This technique allowed the large-scale 3D reconstruction of a volume of mouse hippocampal CA3 tissue at nanometer resolution.

Efficient Carrier Multiplication in Colloidal Silicon Nanorods.

Auger recombination lifetimes, absorption cross sections, and the quantum yields of carrier multiplication (CM), or multiexciton generation (MEG), were determined for solvent-dispersed silicon (Si) nanorods using transient absorption spectroscopy (TAS). Nanorods with an average diameter of 7.5 nm and aspect ratios of 6.1, 19.3, and 33.2 were examined. Colloidal Si nanocrystals of similar diameters were also studied for comparison. The nanocrystals and nanorods were passivated with organic ligands by hydrosilylation to prevent surface oxidation and limit the effects of surface trapping of photoexcited carriers. All samples used in the study exhibited relatively efficient photoluminescence. The Auger lifetimes increased with nanorod length, and the nanorods exhibited higher CM quantum yield and efficiency than the nanocrystals with a similar band gap energy Eg. Beyond a critical length, the CM quantum yield decreases. Nanorods with the aspect ratio of 19.3 had the highest CM quantum yield of 1.6 ± 0.2 at 2.9Eg, which corresponded to a multiexciton yield that was twice as high as observed for the spherical nanocrystals.

Orientationally Ordered Silicon Nanocrystal Cuboctahedra in Superlattices.

Uniform silicon nanocrystals were synthesized with cuboctahedral shape and passivated with 1-dodecene capping ligands. Transmission electron microscopy, electron diffraction, and grazing incidence wide-angle and small-angle X-ray scattering show that these soft cuboctahedra assemble into face-centered cubic superlattices with orientational order. The preferred nanocrystal orientation was found to depend on the orientation of the superlattices on the substrate, indicating that the interactions with the substrate and assembly kinetics can influence the orientation of faceted nanocrystals in superlattices.

A single-step reaction for silicon and germanium nanorods.

A single-step reaction has been developed for colloidal quantum-size silicon (Si) and germanium (Ge) nanorods. The nanorods are formed by solution-liquid-solid (SLS) growth from tin (Sn) seed particles prepared by in situ reduction of a molecular tin(II) complex by trisilane, the reactant for Si nanorod growth. Using the same procedure, Ge nanorods can be grown by including a diphenyl germane reactant. The nanorod length could be adjusted from several nanometers to more than a micrometer without significant increase of diameter by manipulating reactant concentrations.

Lithium ion battery peformance of silicon nanowires with carbon skin.

Silicon (Si) nanomaterials have emerged as a leading candidate for next generation lithium-ion battery anodes. However, the low electrical conductivity of Si requires the use of conductive additives in the anode film. Here we report a solution-based synthesis of Si nanowires with a conductive carbon skin. Without any conductive additive, the Si nanowire electrodes exhibited capacities of over 2000 mA h g(-1) for 100 cycles when cycled at C/10 and over 1200 mA h g(-1) when cycled more rapidly at 1C against Li metal. In situ transmission electron microscopy (TEM) observation reveals that the carbon skin performs dual roles: it speeds lithiation of the Si nanowires significantly, while also constraining the final volume expansion. The present work sheds light on ways to optimize lithium battery performance by smartly tailoring the nanostructure of composition of materials based on silicon and carbon.

Colloidal luminescent silicon nanorods.

Silicon nanorods are grown by trisilane decomposition in hot squalane in the presence of tin (Sn) nanocrystals and dodecylamine. Sn induces solution-liquid-solid nanorod growth with dodecylamine serving as a stabilizing ligand. As-prepared nanorods do not luminesce, but etching with hydrofluoric acid to remove residual surface oxide followed by thermal hydrosilylation with 1-octadecene induces bright photoluminescence with quantum yields of 4-5%. X-ray photoelectron spectroscopy shows that the ligands prevent surface oxidation for months when stored in air.

Precision synthesis of silicon nanowires with crystalline core and amorphous shell.

A synthetic route to crystalline silicon (Si) nanowires with an amorphous Si shell is reported. Trisilane (Si3H8) and Sn(HMDS)2 are decomposed in supercritical toluene at 450 °C. Sn(HMDS)2 creates Sn nanoparticles that seed Si nanowire growth by the supercritical fluid-liquid-solid (SFLS) mechanism. The Si : Sn ratio in the reaction determines the growth of amorphous Si shell. No amorphous shell forms at relatively low Si : Sn ratios of 20 : 1, whereas higher Si : Sn ratio of 40 : 1 leads to significant amorphous shell. We propose that hydrogen evolved from trisilane decomposition etches away the Sn seed particles as nanowires grow, which promotes the amorphous Si shell deposition when the higher Si : Sn ratios are used.

Monodisperse silicon nanocavities and photonic crystals with magnetic response in the optical region.

It is generally accepted that the magnetic component of light has a minor role in the light-matter interaction. The recent discovery of metamaterials has broken this traditional understanding, as both the electric and the magnetic field are key ingredients in metamaterials. The top-down technology used so far employs noble metals with large intrinsic losses. Here we report on a bottom-up approach for processing metamaterials based on suspensions of monodisperse full dielectric silicon nanocavities with a large magnetic response in the near-infrared region. Experimental results and theory show that silicon-colloid-based liquid suspensions and photonic crystals made of two-dimensional arrays of particles have strong magnetic response in the near-infrared region with small optical losses. Our findings might have important implications in the bottom-up processing of large-area low-loss metamaterials working in the near-infrared region.

A facile "dispersion-decomposition" route to metal sulfide nanocrystals.

In this paper, we demonstrate a simple and general "dispersion-decomposition" approach to the synthesis of metal sulfide nanocrystals with the assistance of alkylthiol. This is a direct heating process without precursor injection. By using inorganic metal salts and alkylthiol as the raw materials, high-quality Ag(2)S, Cu(2)S, PbS, Ni(3)S(4), CdS, and ZnS nanocrystals were successfully synthesized. The mechanism study shows that the reaction undergoes two steps. A key intermediate compound, metal thiolate, is generated first. It melts and disperses into the solvent at a relatively low temperature, and then it decomposes into metal sulfide as a single precursor upon heating. This method avoids using toxic phosphine agent and injection during the reaction process. The size and shape of the nanocrystal can be also controlled by the concentration of the reactant and ligands. Furthermore, the optical properties and assembly of the nanocrystals have also been studied. This report provides a facile, direct-heating "dispersion-decomposition" approach to synthesize metal sulfides nanocrystals that has potential for future large-scale synthesis.

Wurtzite Cu2ZnSnS4 nanocrystals: a novel quaternary semiconductor.

A new wurtzite phase Cu(2)ZnSnS(4) was discovered and the corresponding nanocrystals have been successfully synthesized. They have been characterized in detail and showed the photoelectric response, which demonstrated their potential in the application of photovoltaic devices.

Direct synthesis of water-soluble ultrathin CdS nanorods and reversible tuning of the solubility by alkalinity.

Highly water-soluble ultrathin and fluorescent CdS nanorods are directly synthesized with the assistance of PEI. PEI plays as the passivator to control and induce the growth of the CdS nanorods and also makes the as-obtained nanorods dispersible in water. The as-obtained CdS nanorods are quite thin thus showing the quantum effect in the fluorescence. By adjusting the alkalinity of the solution, CdS nanorods can be controlled to precipitate or redisperse in water, which is believed to stem from the variation of the charges of the capping PEI molecules. These results provide an efficient and convenient way to directly synthesize water-soluble and fluorescent nanocrystals, which has potential application in biological labeling and detection.